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To solve the problem of low recognition accuracy and long recognition time in conventional methods for digital moving image feature recognition, a method for digital image feature recognition on mobile platforms based on machine learning is proposed. The support vector machine method is used to cluster the dataset, and then the weight reconstruction method is used to reduce the dimension of digital moving image data. The reduced dimension of the digital moving image is embedded in the coordinates to obtain the reduced dimension of the image data information and complete the feature recognition of the digital moving image. The simulation results show that the recognition accuracy of this method in recognizing features of moving digital images is basically stable at more than 95% and the running time is 0.42s. The recognition effect of the proposed method is good, the recognition efficiency is high, and it has some practical application value. [ABSTRACT FROM AUTHOR]

As the leading mobile phone operating system, Android is an attractive target for malicious applications trying to exploit the system's security vulnerabilities. Although several approaches have been proposed in the research literature for the detection of Android malwares, many of them suffer from issues such as small training datasets, there are few features (most studies are limited to permissions) that ultimately affect their performance. In order to address these issues, we propose an approach combining advanced machine learning techniques and Android vulnerabilities taken from the AndroVul dataset, which contains a novel combination of features for three different vulnerability levels, including dangerous permissions, code smells, and AndroBugs vulnerabilities. Our approach relies on that dataset to train Deep Learning (DL) and Support Vector Machine (SVM) models for the detection of Android malware. Our results show that both models are capable of detecting malware encoded in Android APK files with about 99% accuracy, which is better than the current state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In the process of learning English, the status of spoken language is particularly important, and it is also the most concerned aspect of most English learners. However, the current situation is that due to the limited resources of traditional teachers and the lack of oral practice environment, it is difficult for many learners to effectively improve their English level. Based on this, this study builds a smart English recognition system based on support vector machine. Moreover, this paper introduces a support vector machine to characterize speech signals. In addition, this paper uses feature fusion to map complex nonlinear relationships between features based on support vector machines and establishes a smart English recognition system based on support vector machine. The model can accurately identify the syllables and pronunciations in the words. Moreover, the use of a large-scale corpus based on non-specific people in this article can represent the generality of spoken learner. [ABSTRACT FROM AUTHOR]

3D maps for mobile devices provide more realistic views of environments and serve as better navigation aids. Previous research studies show differences on how 3D maps effect the acquisition of spatial knowledge. This is attributable to the differences in mobile device computational capabilities. Crucial to this is the time it takes for a 3D map dataset to be rendered for a complete navigation task. Different findings suggest different approaches on how to solve the problem of time required for both in-core (inside mobile) and out-core (remote) rendering of 3D datasets. Unfortunately, there have not been sufficient studies regarding the analytical techniques required to show the impact of computational resources required to use 3D maps on mobile devices. This paper uses a Support Vector Machine (SVM) to analytically classify mobile device computational capabilities required for 3D maps that are suitable for use as navigation aids. Fifty different Smart phones were categorized on the basis of their Graphical Processing Unit (GPU), display resolution, memory and size. The result of the proposed classification shows high accuracy. [ABSTRACT FROM AUTHOR]